Business problem
Why this project matters
Created a safety-helmet image classifier using CNNs, transfer learning, fine-tuning, and data augmentation to support automated workplace compliance monitoring.
Project scope
What the work covered
01Inspect image classes and data quality
02Resize, normalize, and augment training images
03Train baseline CNN and transfer-learning models
04Fine-tune selected layers
05Evaluate classification errors and deployment considerations
Workflow
From question to evidence
Business problem
→
Data preparation
→
EDA / feature work
→
Model or analysis
→
Evaluation
→
Business conclusion
Analysis dashboard
Portfolio evidence areas
These bars summarize the emphasis of the completed work; detailed numeric outputs and full notebook cells remain available in the linked repository and HTML report.
Notebook evidence
Selected analysis and report visuals
Sample helmet and no-helmet training images
Class distribution: helmet vs. no helmet
Training and validation recall by epoch
Model evaluation visualization
Additional notebook analysis output
Only project-relevant notebook visuals are displayed; environment warnings and setup screenshots are intentionally excluded.
Conclusions
What the analysis demonstrated
Transfer learning provides a strong starting point for limited image datasets.
Augmentation improves robustness to viewpoint, background, and lighting variation.
False negatives deserve special attention in safety-monitoring applications.
GitHub README preview
Repository at a glance
helmnet-safety-helmet-detection
Computer-vision helmet classification using CNNs, transfer learning, data augmentation, and safety-focused evaluation.
Next step
Discuss a similar project
Use the project inquiry form with HelmNet preselected. Include the business objective, available data, timeline, and desired output.